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http://dx.doi.org/10.7735/ksmte.2015.24.5.502

Characterization of Microscale Drilling Process for Functionally Graded M2-Cu Material Using Design of Experiments  

Sim, Jongwoo (Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology)
Choi, Dae Cheol (Graduate School, Department of Mechanical Engineering, Seoul National University of Science and Technology)
Shin, Ki-Hoon (Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology)
Kim, Hong Seok (Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology)
Publication Information
Journal of the Korean Society of Manufacturing Technology Engineers / v.24, no.5, 2015 , pp. 502-507 More about this Journal
Abstract
In this study, a microscale drilling process was conducted to evaluate the cutting characteristics of functionally graded materials. A mixture of M2 and Cu powders were formed and sintered to produce disk specimens of various compositions. Subsequently, a microscale hole was created in the specimen by using a desktop-size micro-machining system. By using design of experiments and analysis of variance, it was found that the M2-Cu composition, spindle speed, and the interactions between these two factors had significant effects on the magnitude of cutting forces. However, the influence of feed rate on the cutting force was negligible. A mathematical model was established to predict the cutting force under a wide range of process conditions, and the reliability of the model was confirmed experimentally. In addition, it was observed that increasing the wt% of Cu in an M2-Cu specimen increased the high-frequency amplitude of cutting forces.
Keywords
Microscale drilling; Functionally graded material; Cutting force; Design of experiments; Analysis of variance;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 Kim, H. S., Shin, K. H., 2014, Material Pixel-based Process Planning for Layered Manufacturing of Hetrogeneous Objects, International Journal of Precision Engineering and Manufacturing, 15:11 2421-2427.   DOI   ScienceOn
2 Shin, K. H., Natu, H., Dutta, D., Mazumder, J., 2003, A Method for the Design and Fabrication of Hetrogeneous Objects, Materials & Design, 24:5 339-353.   DOI   ScienceOn
3 Beal, V. E., Erasenthiran, P., Hopkinson, N., Dickens, P., Ahrens, C. H., 2006, The Effect of Scanning Strategy on Laser Fusion of Functionally Graded H13/Cu Materials, International Journal of Advanced Manufacturing Technology, 30:9 844-852.   DOI
4 Li, C. L., 2001, A Feature-based Approach to Injection Mould Cooling System Design, Computer Aided Design, 33:14 1073-1090.   DOI   ScienceOn
5 Tadamalle, A. P., Reddy, Y. P., Ramjee, E., 2013, Influence of Laser Welding Process Parameters on Weld Pool Geometry Duty Cycle, Advances in Production Engineering & Management, 8:1 52-60.   DOI
6 Beal, V. E., Erasenthiran, P., Hopkinson, N., Dickens, P., Ahrens, C. H., 2006, Optimisation of Processing Parameters in Laser Fused H13/Cu Materials using Response Surface Method(RSM), Journal of Materials Processing Technology, 174:1-3 145-154.   DOI   ScienceOn
7 Ahn, D. G., Kim, H. W., 2010, Study on the Manufacture of a Thermal Management Mould with Three Different Materials using a Direct Metal Tooling Process, Proceeding of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 224:3 385-402.   DOI
8 Nemat-Alla, M. M., Ata, M. H., Bayoumi, M. R. Khair-Eldeen, W., 2011, Powder Metallurgical Fabrication and Microstructural Investigations of Aluminum/steel Functionally Graded Material, Material Sciences and Applications, 2 1708-1718.   DOI
9 Biermann, D., Menzelm ,A., Bartel, T., Hohne, F., Holtermann, R., Ostwald, R., Sieben, B., Tiffe, M., Zabel A., 2011, Experimental and Computational Investigation of Machining Processes for Functionally Graded Materials, Procedia Engineering, 19 22-27.   DOI   ScienceOn
10 Irgolic, T., Cus, F., Paulic, M., Balic, J., 2014, Prediction of Cutting Forces with Neural Network by Milling Functionally Graded Material, Procedia Engineering, 69 804-813.   DOI
11 Kim, H. S., 2013, Prediction of Cutting Forces and Estimation of Size Effects in End Milling Operations by Determining Instantaneous Cutting Force Constants, Journal of the Korean Society of Manufacturing Technology Engineers, 22:6 1003-1009.   DOI   ScienceOn
12 Jeong, J. S., Shin, K. H., 2014, Property Estimation of Functionally Graded Materials between M2 Tool Steel and Cu Fabricated by Powder Metallurgy, Trans. Korean Soc. Mech. Eng. A, 38:9 9534-958.